1. Python Environments
The following is specific for conda installation, but with small variations applies to everything
With conda, you can create, export, list, remove and update environments, i.e. operational areas with different versions of Python and/or packages installed in them.
It is useful when working with packages or data sets which can slow down or even corrupt Python installation or to test “unstable” updates.
To start create a clone environment of the main one, called root. In a Terminal session:
>> conda create --name workspace --clone root creation of an environment named workspace
>> source activate workspace
>> conda info --envs list of all environments, with current one highlighted with an ‘*’ character
>> Spyder LAUNCH SPYDER in “workspace”
Then back in Terminal mode:
>> source deactivate
>> conda env –help
>> conda remove --name workspace --all
A good example is the package opencv for camera and microscope management and image processing. It requires downgrade of other packages (like numpy) and has the habit of corrupting and slowing down Python after a while: then it is time to kill (remove) it.